No research is done in a void: science is constantly expanding previous hypotheses, building upon past knowledge. We live in a digital age where information is ubiquitous, yet we struggle to preserve accurate machine readable and quantitative descriptions of our research compromising our capacity to use them in our inferences. In the following talk I will show how and why we incorporate assumptions in our studies based on three experiments we have conducted: (i) dissociating metacognitive subdomains in medial and lateral anterior prefrontal cortex, (ii) relating reading comprehension to individual differences in the default mode network, and (iii) exploring neural correlates of the content and form of self-generated thoughts. This will be followed by introducing a new inference method - probabilistic Regions of Interest (pROI) - which allows the use of prior knowledge in the form of a probabilistic map. This approach provides the middle ground between ROI and full brain analysis, by giving researchers more flexibility in formalizing priors. The quality of prior probability maps based on the literature can be improved by using unthresholded statistical maps instead of peak coordinates. To facilitate this we have created NeuroVault.org - a community - wide effort to collect unthresholded statistical maps. Taking the initiative a step further I will describe the concept of data papers - publications purely dedicated to datasets. Together those three mechanisms (pROI, NeuroVault.org and data papers) are a small but significant steps towards better, more reusable and reproducible science.
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a humanās information need.
However, increasingly demand is for data. Data that is needed not for peopleās consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers ā professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a humanās information need.
However, increasingly demand is for data. Data that is needed not for peopleās consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers ā professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
How to hack your brain for effortless learningpebble {code}
Ā
Peter talks about how folks are currently hacking their brain to learn new skills more quickly.
In this talk, Peter will talk about:
How your brain works
Ways to hack your brain
Using virtual reality as a training tool
DurĀing Expo Day selected SumĀmit SponĀsors will showĀcase their latĀest iniĀtiaĀtives and solutions:
-- PreĀview the Future of Brain Health with Anu Acharya, Founder and CEO of Map My Genome
-- The Alzheimerās Research and PreĀvenĀtion FounĀdaĀtion (ARPF): DisĀcuss new sciĀence and preĀvenĀtion iniĀtiaĀtives with PresĀiĀdent Dr. Dharma Singh Khalsa.
-- FitĀBrains (a Rosetta Stone comĀpany): Explore ongoĀing big data research with Conny Lin, Data Research SciĀenĀtist & PolĀicy Analyst.
Presentation @ The 2015 SharpBrains Virtual Summit http://sharpbrains.com/summit-2015/agenda
On the large scale of studying dynamics with MEG: Lessons learned from the Hu...Robert Oostenveld
Ā
As part of the Human Connectome Project (HCP), which includes high-quality fMRI, anatomical MRI, DTi and genetic data from 1200 subjects, we have scanned and investigated a subset of 100 subjects (mostly comprised of pairs of twins) using MEG. The raw data acquired in the HCP has been analyzed using standard pipelines [ref1] and both raw and results at various levels of processing have been shared though the ConnectomeDB [ref2].
Throughout the process of the HCP we have not only analyzed (resting state) MEG data, but also have developed the data analysis protocols, the software and the strategies to achieve reproducible MEG connectivity results. The MEG data analysis software is based on FieldTrip, an open source toolbox [ref3], and is shared alongside the data to allow the analyses to be repeated on independent data.
In this presentation I will outline what the HCP MEG team has learned along the way and I will provide recommendations on what to do and what to avoid in making MEG studies on (resting state) connectivity more reproducible.
1. Larson-Prior LJ, Oostenveld R, Della Penna S, Michalareas G, Prior F, Babajani-Feremi A, Schoffelen JM, Marzetti L, de Pasquale F, Di Pompeo F, Stout J, Woolrich M, Luo Q, Bucholz R, Fries P, Pizzella V, Romani GL, Corbetta M, Snyder AZ; WU-Minn HCP Consortium. Adding dynamics to the Human Connectome Project with MEG. Neuroimage, 2013.
doi:10.1016/j.neuroimage.2013.05.056
2. Hodge MR, Horton W, Brown T, Herrick R, Olsen T, Hileman ME, McKay M, Archie KA, Cler E, Harms MP, Burgess GC, Glasser MF, Elam JS, Curtiss SW, Barch DM, Oostenveld R, Larson-Prior LJ, Ugurbil K, Van Essen DC, Marcus DS. ConnectomeDB-Sharing human brain connectivity data. Neuroimage, 2016. doi:10.1016/j.neuroimage.2015.04.046
3. Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Comput Intell Neurosci. 2011. doi:10.1155/2011/156869
We're curious minds, hackers and tinkerers.
We love to tweak our tools.
But our most important and wonderful tool is our own brain.
How can we understand what's going on so we can hack it?
This is the full-text slide deck for "Hack Your Brain".
You can find all the trivia in "Hack Your Brain - Trivia" and a french (lighter) version in "Hack Your Brain - FR".
Presentation to CRC Mental Health Early Career Researcher Workshop, Melbourne 29.11.17 for @andsdata.
Workshop title: A by-product of scientific training: We're all a little bit biased.
Computational Neuroscience - The Brain - Computer Science InterfaceChristopher Currin
Ā
Understanding intelligence is one of the most challenging scientific problems faced by humanity.
This talk will provide an introduction to the multi-disciplinary field of Computational Neuroscience: the questions it seeks to answer and some of the (mathematical & computational) techniques used to investigate how we fundamentally think.
Currently doing his Computational Neuroscience PhD at the University of Cape Town, Chris has a wonderfully weird background in machine learning, neuroscience, and psychology. He is fascinated by how we think and learn, sometimes to a fault, and how this works in both biological and artificial intelligence.
Data-knowledge transition zones within the biomedical research ecosystemMaryann Martone
Ā
Overview of the Neuroscience Information Framework and how it brings together data, in the form of distributed databases, and knowledge, in the form of ontologies to show the mapping of the dataspace and places where there are mismatches between data and knowledge.
In this talk we will summarise some of the detectable trends on AI beyond deep learning. We will focus on the current transition from deep learning to deep semantics, describing the enabling infrastructures, challenges and opportunities in the construction of the next generation AI systems. The talk will focus on Natural Language Processing (NLP) as an AI sub-domain and will link to the research at the AI Systems Lab at the University of Manchester.
How to hack your brain for effortless learningpebble {code}
Ā
Peter talks about how folks are currently hacking their brain to learn new skills more quickly.
In this talk, Peter will talk about:
How your brain works
Ways to hack your brain
Using virtual reality as a training tool
DurĀing Expo Day selected SumĀmit SponĀsors will showĀcase their latĀest iniĀtiaĀtives and solutions:
-- PreĀview the Future of Brain Health with Anu Acharya, Founder and CEO of Map My Genome
-- The Alzheimerās Research and PreĀvenĀtion FounĀdaĀtion (ARPF): DisĀcuss new sciĀence and preĀvenĀtion iniĀtiaĀtives with PresĀiĀdent Dr. Dharma Singh Khalsa.
-- FitĀBrains (a Rosetta Stone comĀpany): Explore ongoĀing big data research with Conny Lin, Data Research SciĀenĀtist & PolĀicy Analyst.
Presentation @ The 2015 SharpBrains Virtual Summit http://sharpbrains.com/summit-2015/agenda
On the large scale of studying dynamics with MEG: Lessons learned from the Hu...Robert Oostenveld
Ā
As part of the Human Connectome Project (HCP), which includes high-quality fMRI, anatomical MRI, DTi and genetic data from 1200 subjects, we have scanned and investigated a subset of 100 subjects (mostly comprised of pairs of twins) using MEG. The raw data acquired in the HCP has been analyzed using standard pipelines [ref1] and both raw and results at various levels of processing have been shared though the ConnectomeDB [ref2].
Throughout the process of the HCP we have not only analyzed (resting state) MEG data, but also have developed the data analysis protocols, the software and the strategies to achieve reproducible MEG connectivity results. The MEG data analysis software is based on FieldTrip, an open source toolbox [ref3], and is shared alongside the data to allow the analyses to be repeated on independent data.
In this presentation I will outline what the HCP MEG team has learned along the way and I will provide recommendations on what to do and what to avoid in making MEG studies on (resting state) connectivity more reproducible.
1. Larson-Prior LJ, Oostenveld R, Della Penna S, Michalareas G, Prior F, Babajani-Feremi A, Schoffelen JM, Marzetti L, de Pasquale F, Di Pompeo F, Stout J, Woolrich M, Luo Q, Bucholz R, Fries P, Pizzella V, Romani GL, Corbetta M, Snyder AZ; WU-Minn HCP Consortium. Adding dynamics to the Human Connectome Project with MEG. Neuroimage, 2013.
doi:10.1016/j.neuroimage.2013.05.056
2. Hodge MR, Horton W, Brown T, Herrick R, Olsen T, Hileman ME, McKay M, Archie KA, Cler E, Harms MP, Burgess GC, Glasser MF, Elam JS, Curtiss SW, Barch DM, Oostenveld R, Larson-Prior LJ, Ugurbil K, Van Essen DC, Marcus DS. ConnectomeDB-Sharing human brain connectivity data. Neuroimage, 2016. doi:10.1016/j.neuroimage.2015.04.046
3. Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Comput Intell Neurosci. 2011. doi:10.1155/2011/156869
We're curious minds, hackers and tinkerers.
We love to tweak our tools.
But our most important and wonderful tool is our own brain.
How can we understand what's going on so we can hack it?
This is the full-text slide deck for "Hack Your Brain".
You can find all the trivia in "Hack Your Brain - Trivia" and a french (lighter) version in "Hack Your Brain - FR".
Presentation to CRC Mental Health Early Career Researcher Workshop, Melbourne 29.11.17 for @andsdata.
Workshop title: A by-product of scientific training: We're all a little bit biased.
Computational Neuroscience - The Brain - Computer Science InterfaceChristopher Currin
Ā
Understanding intelligence is one of the most challenging scientific problems faced by humanity.
This talk will provide an introduction to the multi-disciplinary field of Computational Neuroscience: the questions it seeks to answer and some of the (mathematical & computational) techniques used to investigate how we fundamentally think.
Currently doing his Computational Neuroscience PhD at the University of Cape Town, Chris has a wonderfully weird background in machine learning, neuroscience, and psychology. He is fascinated by how we think and learn, sometimes to a fault, and how this works in both biological and artificial intelligence.
Data-knowledge transition zones within the biomedical research ecosystemMaryann Martone
Ā
Overview of the Neuroscience Information Framework and how it brings together data, in the form of distributed databases, and knowledge, in the form of ontologies to show the mapping of the dataspace and places where there are mismatches between data and knowledge.
In this talk we will summarise some of the detectable trends on AI beyond deep learning. We will focus on the current transition from deep learning to deep semantics, describing the enabling infrastructures, challenges and opportunities in the construction of the next generation AI systems. The talk will focus on Natural Language Processing (NLP) as an AI sub-domain and will link to the research at the AI Systems Lab at the University of Manchester.
Data Communities - reusable data in and outside your organization.Paul Groth
Ā
Description
Data is a critical both to facilitate an organization and as a product. How can you make that data more usable for both internal and external stakeholders? There are a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data (re)use. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data. I put this in the context of the notion data communities that organizations can use to help foster the use of data both within your organization and externally.
Elsevier CWTS Open Data Report Presentation at RDA meeting in Barcelona Elsevier
Ā
The Open Data report is a result of a year-long, co-conducted study between Elsevier and the Centre for Science and Technology Studies (CWTS), part of Leiden University, the Netherlands. The study is based on a complementary methods approach consisting of a quantitative analysis of bibliometric and publication data, a global survey of 1,200 researchers and three case studies including in-depth interviews with key individuals involved in data collection, analysis and deposition in the fields of soil science, human genetics and digital humanities.
Presentation given at Organization for Human Brain Mapping Annual Meeting in Singapore 2018
Video recording: https://www.pathlms.com/ohbm/courses/8246/sections/12538/video_presentations/116214
Evaluation of full brain parcellation schemes using the NeuroVault database o...Krzysztof Gorgolewski
Ā
Slides from a talk given at SfN 2016.
The task of dividing the human brain into regions has been captivating scientists for many years. In the following work we revisit this challenge and introduce a new evaluation technique that works for both cortical and subcortical parcellations. Our approach is based on data from a diverse set of cognitive experiments that employs nonparametric methods to account for smoothness and parcel size biases.
As reported before parcel variance was a function of parcel size in that smaller parcels were more likely to be homogenous (even in random data). However, when we used map-specific null distributions to account for both smoothness of statistical maps as well as number of parcels in atlases, unbiased estimates become apparent. Both Yeo et al. and Collins et al. parcellations produce scores for random data similar to those derived from real data. In contrast, Shen et al., AAL, and Gordon et al. show lower within parcel variance when applied to real data than when applied to random data (but no distinction can be made between them).
In addition to looking at within parcel variance we also applied a novel metric based on the intuition that different parts of the brain should not only be homogenous, but also different from each other. To quantify this we calculated a ratio of between and within parcel variances (standardized using individual null models). This approach indirectly penalizes parcellations with too many unnecessary parcels. Using this measure we show that Yeo et al. parcellation fits data better (Figure 1) than Collins et al. atlas despite having fewer parcels (7 vs 10).
We present a novel approach to evaluating atlases and parcellations of the human brain that captures diverse patterns observed across many cognitive studies. Our testing methodology overcomes biases introduced by the size of the parcels and smoothness of input data, but also, in contrast to previous methods, can be applied to whole brain volumetric data. We have found that in contrast to previous reports based on resting state cortico cortical connectivity Shen et al. and AAL atlases can delineate brain regions with above average accuracy.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Ā
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Model Attribute Check Company Auto PropertyCeline George
Ā
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Ā
Francesca Gottschalk from the OECDās Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
How to Make a Field invisible in Odoo 17Celine George
Ā
It is possible to hide or invisible some fields in odoo. Commonly using āinvisibleā attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Ā
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Hanās Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insiderās LMA Course, this piece examines the courseās effects via a variety of Tim Han LMA course reviews and Success Insider comments.
A Strategic Approach: GenAI in EducationPeter Windle
Ā
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Ā
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
Ā
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Ā
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Digital Artifact 2 - Investigating Pavilion Designs
Ā
Reusable Science: How not to slip from the shoulders of giants
1. Reusable Science:
How not to slip from
the shoulders of giants
Chris Gorgolewski
Max Planck Research Group: Neuroanatomy &
Connectivity
2. Anatomy of a giant
I. Example studies
II. Probabilistic ROIs
III.Sharing statistical maps
IV.Data papers
3. Anatomy of a giant
I. Example studies
II. Probabilistic ROIs
III.Sharing statistical maps
IV.Data papers
4. Study I
Medial and Lateral Networks in Anterior
Prefrontal Cortex Support Metacognitive
Ability for Memory and Perception
Benjamin Baird, Jonathan Smallwood, Krzysztof J.
Gorgolewski, and Daniel S. Margulies
Journal of Neuroscience (in press)
5. Meta-cognition
ā¢ Are we equally good in judging our
performance of memory or perception
tasks?
ā¢ Is metacognition related to medial or
lateral prefrontal cortex? Does it depend
on modality?
8. Sources of seed points
Gilbert et al. 2006, Functional specialization within rostral prefrontal cortex
(area 10): a meta-analysis. Journal of cognitive neuroscience
9. Sources of seed points
Fleming, S. M., Weil, R. S., Nagy, Z., Dolan, R. J., & Rees, G. (2010).
Relating introspective accuracy to individual differences in brain
structure. Science (New York, N.Y.), 329(5998), 1541ā3.
12. Study II
The Default Modes of Reading: Modulation
of posterior cingulate and medial prefrontal
cortex connectivity associated with
subjective and objective differences in
reading experience
Jonathan Smallwood, Krzysztof J. Gorgolewski, Johannes
Golchert, Florence J.M. Ruby, Haakon G. Engen, Benjamin
Baird, Melaina Vinski, Jonathan Schooler, Daniel S. Margulies
Frontiers in Neuroscience (in press)
13. Reading comprehension
ā¢ What is the relation between task focus
and reading comprehension?
ā¢ What role does Default Mode Network
play in reading comprehension and task
focus?
14. Task focus is inversely correlated
with reading comprehension
15. Reading by Default
Seed locations
Andrews-hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., Buckner, R. L., &
Temp, P. (2010). Functional-anatomic fractionation of the brainās default
network. Neuron, 65(4), 550ā62.
Smallwood, et al., Frontiers in Human Neuroscience
19. Study III
A correspondence between the brain's
intrinsic functional architecture and the
content and form of self-generated
thoughts
Krzysztof J. Gorgolewski, Dan Lurie, Sebastian Urchs,
Judy A. Kipping, R. Cameron Craddock, Michael P. Milham,
Daniel S. Margulies, and Jonathan Smallwood
PLoS One (submitted)
20. Mind wandering
ā¢ What is the content and form of thoughts
in mind wandering?
ā¢ How does it relate to various aspects of
intrinsic BOLD activity?
48. Extensions and disclaimers
ā¢ Kernel density estimation
ā¢ Markov Random Field reguralization
ā¢ Posterior maps cannot be used in meta
analysis ā circularity!
ā¢ Prior maps are integral part of the analysis
and need to be included in publications
49.
50.
51.
52. Anatomy of a giant
I. Example studies
II. Probabilistic ROIs
III.Sharing statistical maps
IV.Data papers
53. Just coordinates?
ā¢ Databases such as Neurosynth or
BrainMap rely on peak coordinates
reported in papers (only strong effects)
58. Data sharing?
ā¢ Ok, ok so we should share data.
ā¢ We all know itās good.
ā¢ But almost no one does it.
ā You have to prepare data
ā You risk that your mistakes will be found!
59. āI swear Iāve heard it beforeā
ā¢ In the past there were many attempts to
propagate data sharing
ā For example fMRI DC:
ā¢ Failed because of technical issues
ā¢ ā¦and the amount of time it took to prepare data
for submission (a week, a very frustrating week)
ā¢ fMRI DC was however too ambitious for its
time:
ā They wanted to collect raw data and all
metadata required to reproduce the analysis
Van Horn & Gazzaniga (2013). Why share data? Lessons learned from the fMRIDC. NeuroImage
60. Baby steps
ā¢ Everything is a question of cost and
benefit
ā If we keep the cost low even small benefit (or
just conviction that data sharing is GOOD) will
suffice
61. NeuroVault.org
simple data sharing
ā¢ Minimize the cost!
ā¢ We just want your statistical maps with
minimum description (DOI)
ā If you want you can put more metadata, but
you donāt have to
ā¢ We streamline login process (external
services such as Google, Facebook)
62. Benefits?
ā¢ In return authors get interactive web
based visualization of their statistical maps
ā Something they can embed on their lab
website
ā¢ We are keeping both cost and benefit
lowā¦
ā ā¦but we also plan to work with journal editors
to popularize the idea
70. Solution ā data papers
ā¢ Authors get recognizable credit for their
work.
ā Even smaller contributors such as RAs can be
included.
ā¢ Acquisition methods are described in
detail.
ā¢ Quality of metadata is being controlled by
peer review.
71.
72. Where to publish data papers?
ā¢ Neuroinformatics (Springer)
ā¢ Frontiers in Human Brain Methods
(Nature Publishing
(Frontiers Media) Group)
ā¢ GigaScience (BGI, BioMed Central)
ā¢ Scientific Data (Nature Publising Group,
coming soon)
73.
74.
75. Read more
ā¢ Probabilistic ROIS
Gorgolewski et al. PRNI, 2013
ā¢ NeuroVault.org
Gorgolewski et al. OHBM, 2013
ā¢ Data papers
Gorgolewski et al. Frontiers in Brain Imaging
Methods, 2012
76. Acknowledgements
(my personal giants)
Pierre-Louie Bazin
Haakon Engen
Satrajit Ghosh
Russell A. Poldrack
Jean-Baptiste Poline
Yannick Schwarz
Tal Yarkoni
Michael Milham
Daniel Margulies
Benjamin Baird
Jonathan Smallwood
Johannes Golchert
Florence J.M. Ruby
Melaina Vinski
Jonathan Schooler
Dan Lurie
Sebastian Urchs
Judy A. Kipping
R. Cameron Craddock
MPI CBS Resting state group
Anterior precuneus, Right inferior parietal cortex
DMN related to SGTs
Addandrewshanna
Positive (right insular cortex, right frontal operculum) vs. negative (striatum) correlations;
PCC hub
Add labels spell out
Mind wandering is related to brain regions that are part of brain networks other than default mode network.
Answer the question directly
It was a picture of a boa constrictor digesting an elephant
Think how much money and effort goes into one study100,000 USD to produce one paper:6-12 pages of authors interpretation of acquired dataā¦without the data itselfBy not reporting subthreshold effects we are wasting (taxpayers) money!
Data sharing is like flossing ā everyone knows is good, but no one does it.